Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible disease which is under-diagnosed, life-threatening and mainly interferes with normal breathing. Individuals who suffer from COPD experience an intense shortness of breath during exercise, which causes a general disability. Daily activities, such as walking can become very difficult due to breathlessness as the condition gradually worsens.
COPD is a respiratory disease that is characterized by inflammation of the airways. COPD is characterized by an airflow limitation that is not fully reversible. The airflow limitation is both progressive and associated with an abnormal inflammatory response of the lungs to noxious particles or gases. Symptoms of COPD may include coughing, wheezing and the production of mucus, and the degree of severity may, in part, be viewed in terms of the volume and color of secretions.
COPD patients can be very prone to lung infections and pneumonia, which can cause a downward spiral of repeated lung infections and a further decline of lung function. Acute exacerbations of COPD have a negative impact on the health related quality of life, pulmonary function and survival of patients with COPD.
Exacerbations are the worsening of COPD symptoms. The exacerbations may be associated with a variable degree of physiological deterioration. The exacerbations may be measured as a decrease in Forced Expiratory Volume measured over one second (FEV1). The exacerbations may be characterized by increased coughing, dyspnea (i.e., shortness of breath) and production of sputum. The major symptom of an exacerbation is the worsening of dyspnea (i.e., shortness of breath) while the main reaction is a lack of energy, which in turn may translate to a reduction in physical activity levels.
The exacerbations are normally caused by viral or bacterial infections and often may lead to hospitalization of the COPD patients. The frequency of exacerbations increases during the winter months due to cold stresses on the patient's body as disclosed in the article “Seasonal distribution of COPD exacerbations in the Prevention of Exacerbations with Tiotropium in COPD trial” by Rabe et al., 2013 March; 143(3):711-9. This may be due to a combination of a) the cooling of facial skin and airways, resulting in bronchoconstriction, and b) the thermoregulatory system becoming less effective with age, thus making COPD patients more susceptible for respiratory infections. The exacerbations not only limit the performance of daily activities, but also significantly decrease the health related quality of life of COPD patients. A high frequency of exacerbations is linked to a poor prognosis for survival. Also, the exacerbations often may result in hospitalization, which is the main determinant of the overall healthcare expenditure for COPD patients.
Because of the damage done when an exacerbation takes place it is desirable to predict the likely onset of an exacerbation and initiate treatments which either prevent the occurring exacerbation and/or treat the symptoms at an early stage thereby reducing the severity and damage caused by the exacerbation. Moreover, reducing and most importantly preventing exacerbations may help COPD patients lead an improved quality of life and may lower the healthcare costs for COPD patients.
To improve quality of COPD patients' lives and reduce healthcare costs by timely treating exacerbations, technologies are requested that reliably enable risk stratification of COPD patients at discharge from the hospital, monitor and support COPD patients' conditions at home, reduce hospital admissions, detect deterioration and reduce early mortality.
Clinically, the diagnosis and disease severity of COPD patients is based on the assessment of spirometric parameters; however, spirometry can be difficult to execute correctly due to patients' inability to perform forced maneuvers. Moreover there is general belief in the medical community that these parameters are insensitive to changes over short periods of time in patients with COPD, so that they may not be a reliable metric for acute respiratory events.
Up to now, no monitoring services are available that enable to assess patients' deterioration due to an exacerbation and/or risk of re-admission and indicate to clinicians, caregivers or family members a risk of an exacerbation and/or readmission of the patient.
Change of activity, in particular, a change in the intensity of a physical activity, is often mentioned as a good measure to detect exacerbations in COPD. So far, numerous approaches have been considered when studying the physical activities of COPD patients, see the article “Physical activity and hospitalization for exacerbation of COPD” by Pitta et al., Chest. 2006 March; 129(3):536-44, the article “Characteristics of physical activities in daily life in chronic obstructive pulmonary disease” by Pitta et al., Am J Respir Crit Care Med. 2005 May 1; 171(9):972-7. Epub 2005 Jan. 21, and the article “Physical activity and clinical and functional status in COPD” by Garcia-Aymerich et al., Chest. 2009 July; 136(1):62-70. doi: 10.1378/chest.08-2532. Epub 2009 Mar. 2. For example, different types of activity (such as, e.g., walking, standing, sitting, running) performed by patients have been actively studied by many researchers. Further, counting the number of steps experienced by the patient has also been looked into. All these approaches however only examine the activity of patients during their active period, such as, e.g., during day time. The symptoms of COPD do not stop when patients go to bed, of course. In many occasions, COPD patients cannot enjoy a good night sleep because of their symptoms (such as, e.g., coughing). Nonetheless, the existing studies mentioned above do not continuously study patients' activity during sleep in the home environment, i.e., during bed time in relation with hospital readmissions. The article “Actigraphic assessment of sleep in chronic obstructive pulmonary disease” by Nunes et al., Sleep Breath, 2013 March; 17(1):125-32, studies twenty-six moderate to very severe COPD patients and fifteen controls by actigraphy for at least five days. COPD patients showed increased sleep latency, mean activity, and reduced total sleep time as compared to the controls.
Various questionnaires have been used in detecting an exacerbation, however disadvantages of this method is that they are highly subjective, rely on memory recall, and must be short to ensure compliance. This can affect the sensitivity of the algorithm in detecting the onset of an exacerbation.
US 2014/012099 A1 discloses an apparatus and methods including sensing at least one parameter of a subject while the subject sleeps. The parameter is analyzed, and a condition of the subject is determined at least in part responsively to the analysis. The subject is alerted to the condition only after the subject awakes. Other applications are also described.
US 2013/310699 A1 discloses a method of monitoring a patient which includes measuring neural respiratory drive using a monitoring device, repeating the measurement either continuously or at regular time intervals, and comparing the measurements obtained in order to predict treatment failure and/clinical deterioration and/or re-admission. The neural respiratory drive is measured by obtaining a measure of the second intercostal space parasternal electromyogram. A monitoring device includes a signal input, a processing unit, and an output unit, and is arranged to measure the neural respiratory drive, store the measured value and compare it to a previously measured value for the neural respiratory drive.
WO 97/12546 A1 discloses a method and an apparatus for assessing cardiovascular risk. The method for assessing risk of an adverse clinical event includes detecting a physiologic signal in the subject and determining from the physiologic signal a sequence of intervals corresponding to time intervals between heart beats. The long-time structure of fluctuations in the intervals over a time period of more than fifteen minutes is analyzed to assess risk of an adverse clinical event. In a preferred embodiment, the physiologic signal is an electrocardiogram and the time period is at least fifteen minutes. A preferred method for analyzing the long-time structure variability in the intervals includes computing the power spectrum and fitting the power spectrum to a power law dependence on frequency over a selected frequency range such as 10−4 to 10−2 Hz. Characteristics of the long-time structure fluctuations in the intervals is used to assess risk of an adverse clinical event.
A method and apparatus for the detection of the onset of hypoglycaemia is described in AU 2012 350348 A1. A portable sensor worn by a subject is used to detect a physiological tremor signal. The tremor signal is analyzed over a period of time, and an alarm is generated when a change in the physiological tremor signal indicative of the onset of hypoglycaemia in the subject is detected. A patient or carer can then perform appropriate action, such as performing a finger prick test to determine blood sugar test and treating as required. The portable sensor can be used to detect a tremor signal indicative of the, the onset of hypoglycaemia such as a signal corresponding to a decrease in blood glucose level (BGL) below 5 mmol/l. The portable sensor can use an accelerometer and may be worn on a limb, such as an arm or leg. The sensor can be used to measure the power of the tremor signal and detect a change, such as an increase in power over time and/or an increase in the rate of change of power over time. Filtering the signal may include filtering signals outside of the range 0-50 Hz, or more specifically 7-15 Hz.
US 2010/0010552 A1 describes a method and a system for temperature analysis to provide an early marker of congestive heart failure progress that precedes a patient's symptoms. The temperature of a patient is a significant predictor of death in heart failure patients. Temperature provides a window into the physiology of the patient's underlying condition and may be used as an early marker for CHF exacerbations. The patient's temperature is taken to form a time series of temperature values. In accordance with some embodiments, the time series of temperature values is converted to the frequency domain by, for example, a discrete Fourier Transform. The frequency domain representation then is analyzed for a marker indicative of the worsening condition of the patient. In accordance with other embodiments, the patient's time series of temperature values is analyzed for a marker using, for example, Cosinor analysis. In yet other embodiments, both the time and frequency domain temperature data is analyzed for markers of the patient's worsening medical condition.
In an observational cohort study of 169 persons with COPD, May M L, Teylan M, Westan N A, Gagnon D R, Garshick E ((2013) Daily Step Count Predicts Acute Exacerbations in a US Cohort with COPD. PLoS ONE 8(4): e60400. doi: 10.1371/journal.pone.0060400) directly assessed physical activating with the StepWatch Activity Monitor, an ankle-worn accelerometer that measures daily step count. We also assessed exercise capacity with the 6-minute walk text (6MWT) and patient-reported PA with the St. George's Respiratory Questionnaire Activity Score (SGRQ-AS) acute exacerbation (AEs). The authors conclude that lower daily step count, lower 6MWT distance, and worse SGRQ-AS predict future AEs and COPD-related hospitalizations, independent of pulmonary function and previous AE history. These results support the importance of assessing PA in patients with COPD, and provide the rationale to promote PA as part of exacerbation-prevention strategies.
An objective of “Classification of Exacerbation Episodes in Chronic Obstructive Pulmonary Disease Patients” by A. Dias et al., Methods of Information in Medicine, Schattauer GmbH, DE, vol. 53, no. 2, 11 Feb. 2014, pages 108-114, is to build computational models capable of distinguishing between normal life days from exacerbation days in COPD patients, based on physical activity measured by accelerometers. The authors recruited 58 patients suffering from COPD and measured their physical activity with accelerometers for 10 days or more, from August 2009 to March 2010. During this period the authors recorded six exacerbation episodes in the patients accounting for 37 days. They were able to analyze data from 52 patients (369 patient days) and extracted three distinct sets of features from the data one set of basic features such as average one set based on the frequency domain and the last exploring the cross in formation among sensors pairs. These were used by three machine-learning techniques (logarithmic regression, neural networks, support vector machines) to distinguish days with exacerbation events from normal days. The support vector machine classifier achieved an AUC of 90%±9, when supplied with a set of features resulting from sequential feature selection method. Neural networks achieved an AUX of 83%±16 and the logarithmic regression an AUC of 67%±15. The authors conclude that none of the individual feature sets provided robust for reasonable classification of PA recording days. The results indicate that this approach has the potential to extract useful information for, but are not robust enough for medical application of the system.
WO 2013/080109 A2 provides for a health monitoring system comprising an activity monitor. The health monitoring system further comprises a processor and a memory for storing machine readable Instructions. The Instructions cause the processor to derive activity counts from the activity data acquired by the activity monitor. The instructions further cause the processor to store the activity counts in the memory, and are associated with a time. The instructions further cause the processor to calculate at least two Statistical parameters from the activity counts, wherein the at least two Statistical parameters are descriptive of the activity counts as a function of time. The instructions further causes the processor to calculate a risk score for each of the at least two Statistical parameters. The instructions further cause the processor to calculate a total risk score using the risk score for each of the at least two Statistical parameters.
The authors of “Circadian Heart Rate Variability in Permanent Atrial Fibrillation Patients” by I. Kurcalte et al. in Electrocardiology 2014—Proceedings of the 41st International Congress on Electrocardiology assume that it is possible to use measurements of circadian heart rate (HR) changes for mortality risk and cardiac autonomic control assessment in permanent atrial fibrillation (PAF) patients. In 327 symptomatic PAF patients (259 non-diabetic, 68 diabetic), exposed to Holter monitoring in 2007-2010, circadian HR variability and Standard Heart Rate Variability (HRV) Time domain indices were calculated and compared in patients who died or survived, and non-diabetic and diabetic patients. Patients were followed for a median period of 39 months (1-60). It was found that circadian HR indices were significantly lower in the dead as compared with alive patients (p<0.001); in diabetic patients as compared with those without diabetes (p<0.01), and in diabetic patients with approved diabetic neuropathy diagnosis (p<0.05). Measured HRV indices didn't show significant differences in studied patients groups. Circadian HR variability showed promising predictive value for risk assessment in PAF patients.